Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Do algorithm animations assist learning?: an empirical study and analysis
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
A strategy for managing content complexity in algorithm animation
ITiCSE '99 Proceedings of the 4th annual SIGCSE/SIGCUE ITiCSE conference on Innovation and technology in computer science education
Algorithm simulation with automatic assessment
Proceedings of the 5th annual SIGCSE/SIGCUE ITiCSEconference on Innovation and technology in computer science education
Animation of user algorithms on the Web
VL '97 Proceedings of the 1997 IEEE Symposium on Visual Languages (VL '97)
Toward effective algorithm visualization artifacts: designing for participation and communication in an undergraduate algorithms course
Future Perspectives - Introduction
Revised Lectures on Software Visualization, International Seminar
Exploring the role of visualization and engagement in computer science education
Working group reports from ITiCSE on Innovation and technology in computer science education
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Visualization of an algorithm offers only a rough picture of operations. Explanations are crucial for deeper understanding, because they help the viewer to associate the visualization with the factual meaning of each detail. We present a framework based on path testing for associating instructive explanations and assignments with a constructive self-study visualization of an algorithm. The algorithm is divided into blocks, and a description is given for each block. The system contains a separate window for code, flowchart, animation, explanations, and control. Assignments are based on the flowchart and on the coverage conditions of path testing. Path testing is expected to lead into more accurate evaluation of learning outcomes because it supports systematic instruction in addition to more free trial-and-error heuristics. A qualitative analysis of preliminary experiences with the prototype indicates that the approach helps a student to reflect on her own reasoning about the algorithm. However, a prerequisite for an successful learning process with the environment is a motivating introduction, describing both the system and the main idea of the algorithm to be learned.